{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f95eeb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "75a82793",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first_dong\n",
      "광장동_강수_202201_202212.csv\n",
      "      day    hour   강수  month address\n",
      "0       1     0.0  0.0      1     광장동\n",
      "1       1   100.0  0.0      1     광장동\n",
      "2       1   200.0  0.0      1     광장동\n",
      "3       1   300.0  0.0      1     광장동\n",
      "4       1   400.0  0.0      1     광장동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.0     12     광장동\n",
      "8767   31  2000.0  0.0     12     광장동\n",
      "8768   31  2100.0  0.0     12     광장동\n",
      "8769   31  2200.0  0.0     12     광장동\n",
      "8770   31  2300.0  0.0     12     광장동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "광장동_기온_202201_202212.csv\n",
      "      day    hour   기온  month address\n",
      "0       1     0.0 -6.5      1     광장동\n",
      "1       1   100.0 -5.3      1     광장동\n",
      "2       1   200.0 -3.0      1     광장동\n",
      "3       1   300.0 -1.9      1     광장동\n",
      "4       1   400.0 -0.2      1     광장동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.3     12     광장동\n",
      "8767   31  2000.0  0.3     12     광장동\n",
      "8768   31  2100.0 -1.2     12     광장동\n",
      "8769   31  2200.0 -1.0     12     광장동\n",
      "8770   31  2300.0  0.0     12     광장동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "광장동_습도_202201_202212.csv\n",
      "      day    hour    습도  month address\n",
      "0       1     0.0  40.0      1     광장동\n",
      "1       1   100.0  30.0      1     광장동\n",
      "2       1   200.0  27.0      1     광장동\n",
      "3       1   300.0  25.0      1     광장동\n",
      "4       1   400.0  29.0      1     광장동\n",
      "...   ...     ...   ...    ...     ...\n",
      "8766   31  1900.0  48.0     12     광장동\n",
      "8767   31  2000.0  50.0     12     광장동\n",
      "8768   31  2100.0  63.0     12     광장동\n",
      "8769   31  2200.0  64.0     12     광장동\n",
      "8770   31  2300.0  58.0     12     광장동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "광장동_풍속_202201_202212.csv\n",
      "      day    hour   풍속  month address\n",
      "0       1     0.0  1.0      1     광장동\n",
      "1       1   100.0  0.3      1     광장동\n",
      "2       1   200.0  0.7      1     광장동\n",
      "3       1   300.0  0.7      1     광장동\n",
      "4       1   400.0  1.0      1     광장동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.4     12     광장동\n",
      "8767   31  2000.0  0.9     12     광장동\n",
      "8768   31  2100.0  0.6     12     광장동\n",
      "8769   31  2200.0  0.9     12     광장동\n",
      "8770   31  2300.0  1.4     12     광장동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "광장동_풍향_202201_202212.csv\n",
      "      day    hour     풍향  month address\n",
      "0       1     0.0  103.0      1     광장동\n",
      "1       1   100.0   71.0      1     광장동\n",
      "2       1   200.0  156.0      1     광장동\n",
      "3       1   300.0   59.0      1     광장동\n",
      "4       1   400.0   59.0      1     광장동\n",
      "...   ...     ...    ...    ...     ...\n",
      "8766   31  1900.0  292.0     12     광장동\n",
      "8767   31  2000.0  268.0     12     광장동\n",
      "8768   31  2100.0  275.0     12     광장동\n",
      "8769   31  2200.0  267.0     12     광장동\n",
      "8770   31  2300.0  261.0     12     광장동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "first_dong\n",
      "구의제1동_강수_202201_202212.csv\n",
      "      day    hour   강수  month address\n",
      "0       1     0.0  0.0      1   구의제1동\n",
      "1       1   100.0  0.0      1   구의제1동\n",
      "2       1   200.0  0.0      1   구의제1동\n",
      "3       1   300.0  0.0      1   구의제1동\n",
      "4       1   400.0  0.0      1   구의제1동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.0     12   구의제1동\n",
      "8767   31  2000.0  0.0     12   구의제1동\n",
      "8768   31  2100.0  0.0     12   구의제1동\n",
      "8769   31  2200.0  0.0     12   구의제1동\n",
      "8770   31  2300.0  0.0     12   구의제1동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "구의제1동_기온_202201_202212.csv\n",
      "      day    hour   기온  month address\n",
      "0       1     0.0 -6.5      1   구의제1동\n",
      "1       1   100.0 -5.3      1   구의제1동\n",
      "2       1   200.0 -3.0      1   구의제1동\n",
      "3       1   300.0 -1.9      1   구의제1동\n",
      "4       1   400.0 -0.2      1   구의제1동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.3     12   구의제1동\n",
      "8767   31  2000.0  0.3     12   구의제1동\n",
      "8768   31  2100.0 -1.2     12   구의제1동\n",
      "8769   31  2200.0 -1.0     12   구의제1동\n",
      "8770   31  2300.0  0.0     12   구의제1동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "구의제1동_습도_202201_202212.csv\n",
      "      day    hour    습도  month address\n",
      "0       1     0.0  40.0      1   구의제1동\n",
      "1       1   100.0  30.0      1   구의제1동\n",
      "2       1   200.0  27.0      1   구의제1동\n",
      "3       1   300.0  25.0      1   구의제1동\n",
      "4       1   400.0  29.0      1   구의제1동\n",
      "...   ...     ...   ...    ...     ...\n",
      "8766   31  1900.0  48.0     12   구의제1동\n",
      "8767   31  2000.0  50.0     12   구의제1동\n",
      "8768   31  2100.0  63.0     12   구의제1동\n",
      "8769   31  2200.0  64.0     12   구의제1동\n",
      "8770   31  2300.0  58.0     12   구의제1동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "구의제1동_풍속_202201_202212.csv\n",
      "      day   hour          풍속  month address\n",
      "0       1   0000   1.000000       1   구의제1동\n",
      "1       1   0100   0.300000       1   구의제1동\n",
      "2       1   0200   0.700000       1   구의제1동\n",
      "3       1   0300   0.700000       1   구의제1동\n",
      "4       1   0400   1.000000       1   구의제1동\n",
      "...   ...    ...         ...    ...     ...\n",
      "8766   31   1900   1.400000      12   구의제1동\n",
      "8767   31   2000   0.900000      12   구의제1동\n",
      "8768   31   2100   0.600000      12   구의제1동\n",
      "8769   31   2200   0.900000      12   구의제1동\n",
      "8770   31   2300   1.400000      12   구의제1동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "구의제1동_풍향_202201_202212.csv\n",
      "      day    hour     풍향  month address\n",
      "0       1     0.0  103.0      1   구의제1동\n",
      "1       1   100.0   71.0      1   구의제1동\n",
      "2       1   200.0  156.0      1   구의제1동\n",
      "3       1   300.0   59.0      1   구의제1동\n",
      "4       1   400.0   59.0      1   구의제1동\n",
      "...   ...     ...    ...    ...     ...\n",
      "8766   31  1900.0  292.0     12   구의제1동\n",
      "8767   31  2000.0  268.0     12   구의제1동\n",
      "8768   31  2100.0  275.0     12   구의제1동\n",
      "8769   31  2200.0  267.0     12   구의제1동\n",
      "8770   31  2300.0  261.0     12   구의제1동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "first_dong\n",
      "구의제2동_강수_202201_202212.csv\n",
      "      day    hour   강수  month address\n",
      "0       1     0.0  0.0      1   구의제2동\n",
      "1       1   100.0  0.0      1   구의제2동\n",
      "2       1   200.0  0.0      1   구의제2동\n",
      "3       1   300.0  0.0      1   구의제2동\n",
      "4       1   400.0  0.0      1   구의제2동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.0     12   구의제2동\n",
      "8767   31  2000.0  0.0     12   구의제2동\n",
      "8768   31  2100.0  0.0     12   구의제2동\n",
      "8769   31  2200.0  0.0     12   구의제2동\n",
      "8770   31  2300.0  0.0     12   구의제2동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "구의제2동_기온_202201_202212.csv\n",
      "      day    hour   기온  month address\n",
      "0       1     0.0 -6.5      1   구의제2동\n",
      "1       1   100.0 -5.3      1   구의제2동\n",
      "2       1   200.0 -3.0      1   구의제2동\n",
      "3       1   300.0 -1.9      1   구의제2동\n",
      "4       1   400.0 -0.2      1   구의제2동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.3     12   구의제2동\n",
      "8767   31  2000.0  0.3     12   구의제2동\n",
      "8768   31  2100.0 -1.2     12   구의제2동\n",
      "8769   31  2200.0 -1.0     12   구의제2동\n",
      "8770   31  2300.0  0.0     12   구의제2동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "구의제2동_습도_202201_202212.csv\n",
      "      day    hour    습도  month address\n",
      "0       1     0.0  40.0      1   구의제2동\n",
      "1       1   100.0  30.0      1   구의제2동\n",
      "2       1   200.0  27.0      1   구의제2동\n",
      "3       1   300.0  25.0      1   구의제2동\n",
      "4       1   400.0  29.0      1   구의제2동\n",
      "...   ...     ...   ...    ...     ...\n",
      "8766   31  1900.0  48.0     12   구의제2동\n",
      "8767   31  2000.0  50.0     12   구의제2동\n",
      "8768   31  2100.0  63.0     12   구의제2동\n",
      "8769   31  2200.0  64.0     12   구의제2동\n",
      "8770   31  2300.0  58.0     12   구의제2동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "구의제2동_풍속_202201_202212.csv\n",
      "      day   hour          풍속  month address\n",
      "0       1   0000   1.000000       1   구의제2동\n",
      "1       1   0100   0.300000       1   구의제2동\n",
      "2       1   0200   0.700000       1   구의제2동\n",
      "3       1   0300   0.700000       1   구의제2동\n",
      "4       1   0400   1.000000       1   구의제2동\n",
      "...   ...    ...         ...    ...     ...\n",
      "8766   31   1900   1.400000      12   구의제2동\n",
      "8767   31   2000   0.900000      12   구의제2동\n",
      "8768   31   2100   0.600000      12   구의제2동\n",
      "8769   31   2200   0.900000      12   구의제2동\n",
      "8770   31   2300   1.400000      12   구의제2동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "구의제2동_풍향_202201_202212.csv\n",
      "      day    hour     풍향  month address\n",
      "0       1     0.0  103.0      1   구의제2동\n",
      "1       1   100.0   71.0      1   구의제2동\n",
      "2       1   200.0  156.0      1   구의제2동\n",
      "3       1   300.0   59.0      1   구의제2동\n",
      "4       1   400.0   59.0      1   구의제2동\n",
      "...   ...     ...    ...    ...     ...\n",
      "8766   31  1900.0  292.0     12   구의제2동\n",
      "8767   31  2000.0  268.0     12   구의제2동\n",
      "8768   31  2100.0  275.0     12   구의제2동\n",
      "8769   31  2200.0  267.0     12   구의제2동\n",
      "8770   31  2300.0  261.0     12   구의제2동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "first_dong\n",
      "구의제3동_강수_202201_202212.csv\n",
      "      day    hour   강수  month address\n",
      "0       1     0.0  0.0      1   구의제3동\n",
      "1       1   100.0  0.0      1   구의제3동\n",
      "2       1   200.0  0.0      1   구의제3동\n",
      "3       1   300.0  0.0      1   구의제3동\n",
      "4       1   400.0  0.0      1   구의제3동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.0     12   구의제3동\n",
      "8767   31  2000.0  0.0     12   구의제3동\n",
      "8768   31  2100.0  0.0     12   구의제3동\n",
      "8769   31  2200.0  0.0     12   구의제3동\n",
      "8770   31  2300.0  0.0     12   구의제3동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "구의제3동_기온_202201_202212.csv\n",
      "      day    hour   기온  month address\n",
      "0       1     0.0 -6.5      1   구의제3동\n",
      "1       1   100.0 -5.3      1   구의제3동\n",
      "2       1   200.0 -3.0      1   구의제3동\n",
      "3       1   300.0 -1.9      1   구의제3동\n",
      "4       1   400.0 -0.2      1   구의제3동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.3     12   구의제3동\n",
      "8767   31  2000.0  0.3     12   구의제3동\n",
      "8768   31  2100.0 -1.2     12   구의제3동\n",
      "8769   31  2200.0 -1.0     12   구의제3동\n",
      "8770   31  2300.0  0.0     12   구의제3동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "구의제3동_습도_202201_202212.csv\n",
      "      day    hour    습도  month address\n",
      "0       1     0.0  40.0      1   구의제3동\n",
      "1       1   100.0  30.0      1   구의제3동\n",
      "2       1   200.0  27.0      1   구의제3동\n",
      "3       1   300.0  25.0      1   구의제3동\n",
      "4       1   400.0  29.0      1   구의제3동\n",
      "...   ...     ...   ...    ...     ...\n",
      "8766   31  1900.0  48.0     12   구의제3동\n",
      "8767   31  2000.0  50.0     12   구의제3동\n",
      "8768   31  2100.0  63.0     12   구의제3동\n",
      "8769   31  2200.0  64.0     12   구의제3동\n",
      "8770   31  2300.0  58.0     12   구의제3동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "구의제3동_풍속_202201_202212.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      day    hour   풍속  month address\n",
      "0       1     0.0  1.0      1   구의제3동\n",
      "1       1   100.0  0.3      1   구의제3동\n",
      "2       1   200.0  0.7      1   구의제3동\n",
      "3       1   300.0  0.7      1   구의제3동\n",
      "4       1   400.0  1.0      1   구의제3동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.4     12   구의제3동\n",
      "8767   31  2000.0  0.9     12   구의제3동\n",
      "8768   31  2100.0  0.6     12   구의제3동\n",
      "8769   31  2200.0  0.9     12   구의제3동\n",
      "8770   31  2300.0  1.4     12   구의제3동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "구의제3동_풍향_202201_202212.csv\n",
      "      day    hour     풍향  month address\n",
      "0       1     0.0  103.0      1   구의제3동\n",
      "1       1   100.0   71.0      1   구의제3동\n",
      "2       1   200.0  156.0      1   구의제3동\n",
      "3       1   300.0   59.0      1   구의제3동\n",
      "4       1   400.0   59.0      1   구의제3동\n",
      "...   ...     ...    ...    ...     ...\n",
      "8766   31  1900.0  292.0     12   구의제3동\n",
      "8767   31  2000.0  268.0     12   구의제3동\n",
      "8768   31  2100.0  275.0     12   구의제3동\n",
      "8769   31  2200.0  267.0     12   구의제3동\n",
      "8770   31  2300.0  261.0     12   구의제3동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "first_dong\n",
      "군자동_강수_202201_202212.csv\n",
      "      day    hour   강수  month address\n",
      "0       1     0.0  0.0      1     군자동\n",
      "1       1   100.0  0.0      1     군자동\n",
      "2       1   200.0  0.0      1     군자동\n",
      "3       1   300.0  0.0      1     군자동\n",
      "4       1   400.0  0.0      1     군자동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.0     12     군자동\n",
      "8767   31  2000.0  0.0     12     군자동\n",
      "8768   31  2100.0  0.0     12     군자동\n",
      "8769   31  2200.0  0.0     12     군자동\n",
      "8770   31  2300.0  0.0     12     군자동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "군자동_기온_202201_202212.csv\n",
      "      day    hour   기온  month address\n",
      "0       1     0.0 -6.5      1     군자동\n",
      "1       1   100.0 -5.3      1     군자동\n",
      "2       1   200.0 -3.0      1     군자동\n",
      "3       1   300.0 -1.9      1     군자동\n",
      "4       1   400.0 -0.2      1     군자동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.3     12     군자동\n",
      "8767   31  2000.0  0.3     12     군자동\n",
      "8768   31  2100.0 -1.2     12     군자동\n",
      "8769   31  2200.0 -1.0     12     군자동\n",
      "8770   31  2300.0  0.0     12     군자동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "군자동_습도_202201_202212.csv\n",
      "      day    hour    습도  month address\n",
      "0       1     0.0  40.0      1     군자동\n",
      "1       1   100.0  30.0      1     군자동\n",
      "2       1   200.0  27.0      1     군자동\n",
      "3       1   300.0  25.0      1     군자동\n",
      "4       1   400.0  29.0      1     군자동\n",
      "...   ...     ...   ...    ...     ...\n",
      "8766   31  1900.0  48.0     12     군자동\n",
      "8767   31  2000.0  50.0     12     군자동\n",
      "8768   31  2100.0  63.0     12     군자동\n",
      "8769   31  2200.0  64.0     12     군자동\n",
      "8770   31  2300.0  58.0     12     군자동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "군자동_풍속_202201_202212.csv\n",
      "      day    hour   풍속  month address\n",
      "0       1     0.0  1.0      1     군자동\n",
      "1       1   100.0  0.3      1     군자동\n",
      "2       1   200.0  0.7      1     군자동\n",
      "3       1   300.0  0.7      1     군자동\n",
      "4       1   400.0  1.0      1     군자동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.4     12     군자동\n",
      "8767   31  2000.0  0.9     12     군자동\n",
      "8768   31  2100.0  0.6     12     군자동\n",
      "8769   31  2200.0  0.9     12     군자동\n",
      "8770   31  2300.0  1.4     12     군자동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "군자동_풍향_202201_202212.csv\n",
      "      day    hour     풍향  month address\n",
      "0       1     0.0  103.0      1     군자동\n",
      "1       1   100.0   71.0      1     군자동\n",
      "2       1   200.0  156.0      1     군자동\n",
      "3       1   300.0   59.0      1     군자동\n",
      "4       1   400.0   59.0      1     군자동\n",
      "...   ...     ...    ...    ...     ...\n",
      "8766   31  1900.0  292.0     12     군자동\n",
      "8767   31  2000.0  268.0     12     군자동\n",
      "8768   31  2100.0  275.0     12     군자동\n",
      "8769   31  2200.0  267.0     12     군자동\n",
      "8770   31  2300.0  261.0     12     군자동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "first_dong\n",
      "능동_강수_202201_202212.csv\n",
      "      day    hour   강수  month address\n",
      "0       1     0.0  0.0      1      능동\n",
      "1       1   100.0  0.0      1      능동\n",
      "2       1   200.0  0.0      1      능동\n",
      "3       1   300.0  0.0      1      능동\n",
      "4       1   400.0  0.0      1      능동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.0     12      능동\n",
      "8767   31  2000.0  0.0     12      능동\n",
      "8768   31  2100.0  0.0     12      능동\n",
      "8769   31  2200.0  0.0     12      능동\n",
      "8770   31  2300.0  0.0     12      능동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "능동_기온_202201_202212.csv\n",
      "      day    hour   기온  month address\n",
      "0       1     0.0 -6.5      1      능동\n",
      "1       1   100.0 -5.3      1      능동\n",
      "2       1   200.0 -3.0      1      능동\n",
      "3       1   300.0 -1.9      1      능동\n",
      "4       1   400.0 -0.2      1      능동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.3     12      능동\n",
      "8767   31  2000.0  0.3     12      능동\n",
      "8768   31  2100.0 -1.2     12      능동\n",
      "8769   31  2200.0 -1.0     12      능동\n",
      "8770   31  2300.0  0.0     12      능동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "능동_습도_202201_202212.csv\n",
      "      day    hour    습도  month address\n",
      "0       1     0.0  40.0      1      능동\n",
      "1       1   100.0  30.0      1      능동\n",
      "2       1   200.0  27.0      1      능동\n",
      "3       1   300.0  25.0      1      능동\n",
      "4       1   400.0  29.0      1      능동\n",
      "...   ...     ...   ...    ...     ...\n",
      "8766   31  1900.0  48.0     12      능동\n",
      "8767   31  2000.0  50.0     12      능동\n",
      "8768   31  2100.0  63.0     12      능동\n",
      "8769   31  2200.0  64.0     12      능동\n",
      "8770   31  2300.0  58.0     12      능동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "능동_풍속_202201_202212.csv\n",
      "      day    hour   풍속  month address\n",
      "0       1     0.0  1.0      1      능동\n",
      "1       1   100.0  0.3      1      능동\n",
      "2       1   200.0  0.7      1      능동\n",
      "3       1   300.0  0.7      1      능동\n",
      "4       1   400.0  1.0      1      능동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.4     12      능동\n",
      "8767   31  2000.0  0.9     12      능동\n",
      "8768   31  2100.0  0.6     12      능동\n",
      "8769   31  2200.0  0.9     12      능동\n",
      "8770   31  2300.0  1.4     12      능동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "능동_풍향_202201_202212.csv\n",
      "      day    hour     풍향  month address\n",
      "0       1     0.0  103.0      1      능동\n",
      "1       1   100.0   71.0      1      능동\n",
      "2       1   200.0  156.0      1      능동\n",
      "3       1   300.0   59.0      1      능동\n",
      "4       1   400.0   59.0      1      능동\n",
      "...   ...     ...    ...    ...     ...\n",
      "8766   31  1900.0  292.0     12      능동\n",
      "8767   31  2000.0  268.0     12      능동\n",
      "8768   31  2100.0  275.0     12      능동\n",
      "8769   31  2200.0  267.0     12      능동\n",
      "8770   31  2300.0  261.0     12      능동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "first_dong\n",
      "중곡제1동_강수_202201_202212.csv\n",
      "      day    hour   강수  month address\n",
      "0       1     0.0  0.0      1   중곡제1동\n",
      "1       1   100.0  0.0      1   중곡제1동\n",
      "2       1   200.0  0.0      1   중곡제1동\n",
      "3       1   300.0  0.0      1   중곡제1동\n",
      "4       1   400.0  0.0      1   중곡제1동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.0     12   중곡제1동\n",
      "8767   31  2000.0  0.0     12   중곡제1동\n",
      "8768   31  2100.0  0.0     12   중곡제1동\n",
      "8769   31  2200.0  0.0     12   중곡제1동\n",
      "8770   31  2300.0  0.0     12   중곡제1동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "중곡제1동_기온_202201_202212.csv\n",
      "      day    hour   기온  month address\n",
      "0       1     0.0 -6.1      1   중곡제1동\n",
      "1       1   100.0 -4.3      1   중곡제1동\n",
      "2       1   200.0 -2.5      1   중곡제1동\n",
      "3       1   300.0 -0.9      1   중곡제1동\n",
      "4       1   400.0 -0.3      1   중곡제1동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.8     12   중곡제1동\n",
      "8767   31  2000.0  0.9     12   중곡제1동\n",
      "8768   31  2100.0 -0.4     12   중곡제1동\n",
      "8769   31  2200.0  0.0     12   중곡제1동\n",
      "8770   31  2300.0  0.4     12   중곡제1동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "중곡제1동_습도_202201_202212.csv\n",
      "      day    hour    습도  month address\n",
      "0       1     0.0  35.0      1   중곡제1동\n",
      "1       1   100.0  30.0      1   중곡제1동\n",
      "2       1   200.0  26.0      1   중곡제1동\n",
      "3       1   300.0  26.0      1   중곡제1동\n",
      "4       1   400.0  29.0      1   중곡제1동\n",
      "...   ...     ...   ...    ...     ...\n",
      "8766   31  1900.0  46.0     12   중곡제1동\n",
      "8767   31  2000.0  49.0     12   중곡제1동\n",
      "8768   31  2100.0  58.0     12   중곡제1동\n",
      "8769   31  2200.0  56.0     12   중곡제1동\n",
      "8770   31  2300.0  57.0     12   중곡제1동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "중곡제1동_풍속_202201_202212.csv\n",
      "      day    hour   풍속  month address\n",
      "0       1     0.0  1.0      1   중곡제1동\n",
      "1       1   100.0  0.8      1   중곡제1동\n",
      "2       1   200.0  0.9      1   중곡제1동\n",
      "3       1   300.0  0.8      1   중곡제1동\n",
      "4       1   400.0  1.0      1   중곡제1동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.9     12   중곡제1동\n",
      "8767   31  2000.0  1.4     12   중곡제1동\n",
      "8768   31  2100.0  0.3     12   중곡제1동\n",
      "8769   31  2200.0  0.7     12   중곡제1동\n",
      "8770   31  2300.0  1.1     12   중곡제1동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "중곡제1동_풍향_202201_202212.csv\n",
      "      day    hour     풍향  month address\n",
      "0       1     0.0   90.0      1   중곡제1동\n",
      "1       1   100.0   19.0      1   중곡제1동\n",
      "2       1   200.0  143.0      1   중곡제1동\n",
      "3       1   300.0  309.0      1   중곡제1동\n",
      "4       1   400.0   54.0      1   중곡제1동\n",
      "...   ...     ...    ...    ...     ...\n",
      "8766   31  1900.0  303.0     12   중곡제1동\n",
      "8767   31  2000.0  314.0     12   중곡제1동\n",
      "8768   31  2100.0   97.0     12   중곡제1동\n",
      "8769   31  2200.0  275.0     12   중곡제1동\n",
      "8770   31  2300.0  128.0     12   중곡제1동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "first_dong\n",
      "중곡제2동_강수_202201_202212.csv\n",
      "      day    hour   강수  month address\n",
      "0       1     0.0  0.0      1   중곡제2동\n",
      "1       1   100.0  0.0      1   중곡제2동\n",
      "2       1   200.0  0.0      1   중곡제2동\n",
      "3       1   300.0  0.0      1   중곡제2동\n",
      "4       1   400.0  0.0      1   중곡제2동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.0     12   중곡제2동\n",
      "8767   31  2000.0  0.0     12   중곡제2동\n",
      "8768   31  2100.0  0.0     12   중곡제2동\n",
      "8769   31  2200.0  0.0     12   중곡제2동\n",
      "8770   31  2300.0  0.0     12   중곡제2동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "중곡제2동_기온_202201_202212.csv\n",
      "      day    hour   기온  month address\n",
      "0       1     0.0 -6.1      1   중곡제2동\n",
      "1       1   100.0 -4.3      1   중곡제2동\n",
      "2       1   200.0 -2.5      1   중곡제2동\n",
      "3       1   300.0 -0.9      1   중곡제2동\n",
      "4       1   400.0 -0.3      1   중곡제2동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.8     12   중곡제2동\n",
      "8767   31  2000.0  0.9     12   중곡제2동\n",
      "8768   31  2100.0 -0.4     12   중곡제2동\n",
      "8769   31  2200.0  0.0     12   중곡제2동\n",
      "8770   31  2300.0  0.4     12   중곡제2동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "중곡제2동_습도_202201_202212.csv\n",
      "      day    hour    습도  month address\n",
      "0       1     0.0  35.0      1   중곡제2동\n",
      "1       1   100.0  30.0      1   중곡제2동\n",
      "2       1   200.0  26.0      1   중곡제2동\n",
      "3       1   300.0  26.0      1   중곡제2동\n",
      "4       1   400.0  29.0      1   중곡제2동\n",
      "...   ...     ...   ...    ...     ...\n",
      "8766   31  1900.0  46.0     12   중곡제2동\n",
      "8767   31  2000.0  49.0     12   중곡제2동\n",
      "8768   31  2100.0  58.0     12   중곡제2동\n",
      "8769   31  2200.0  56.0     12   중곡제2동\n",
      "8770   31  2300.0  57.0     12   중곡제2동\n",
      "\n",
      "[8771 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "중곡제2동_풍속_202201_202212.csv\n",
      "      day    hour   풍속  month address\n",
      "0       1     0.0  1.0      1   중곡제2동\n",
      "1       1   100.0  0.8      1   중곡제2동\n",
      "2       1   200.0  0.9      1   중곡제2동\n",
      "3       1   300.0  0.8      1   중곡제2동\n",
      "4       1   400.0  1.0      1   중곡제2동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.9     12   중곡제2동\n",
      "8767   31  2000.0  1.4     12   중곡제2동\n",
      "8768   31  2100.0  0.3     12   중곡제2동\n",
      "8769   31  2200.0  0.7     12   중곡제2동\n",
      "8770   31  2300.0  1.1     12   중곡제2동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "중곡제2동_풍향_202201_202212.csv\n",
      "      day    hour     풍향  month address\n",
      "0       1     0.0   90.0      1   중곡제2동\n",
      "1       1   100.0   19.0      1   중곡제2동\n",
      "2       1   200.0  143.0      1   중곡제2동\n",
      "3       1   300.0  309.0      1   중곡제2동\n",
      "4       1   400.0   54.0      1   중곡제2동\n",
      "...   ...     ...    ...    ...     ...\n",
      "8766   31  1900.0  303.0     12   중곡제2동\n",
      "8767   31  2000.0  314.0     12   중곡제2동\n",
      "8768   31  2100.0   97.0     12   중곡제2동\n",
      "8769   31  2200.0  275.0     12   중곡제2동\n",
      "8770   31  2300.0  128.0     12   중곡제2동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "first_dong\n",
      "중곡제3동_강수_202201_202212.csv\n",
      "      day    hour   강수  month address\n",
      "0       1     0.0  0.0      1   중곡제3동\n",
      "1       1   100.0  0.0      1   중곡제3동\n",
      "2       1   200.0  0.0      1   중곡제3동\n",
      "3       1   300.0  0.0      1   중곡제3동\n",
      "4       1   400.0  0.0      1   중곡제3동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.0     12   중곡제3동\n",
      "8767   31  2000.0  0.0     12   중곡제3동\n",
      "8768   31  2100.0  0.0     12   중곡제3동\n",
      "8769   31  2200.0  0.0     12   중곡제3동\n",
      "8770   31  2300.0  0.0     12   중곡제3동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "중곡제3동_기온_202201_202212.csv\n",
      "      day    hour   기온  month address\n",
      "0       1     0.0 -6.1      1   중곡제3동\n",
      "1       1   100.0 -4.3      1   중곡제3동\n",
      "2       1   200.0 -2.5      1   중곡제3동\n",
      "3       1   300.0 -0.9      1   중곡제3동\n",
      "4       1   400.0 -0.3      1   중곡제3동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.8     12   중곡제3동\n",
      "8767   31  2000.0  0.9     12   중곡제3동\n",
      "8768   31  2100.0 -0.4     12   중곡제3동\n",
      "8769   31  2200.0  0.0     12   중곡제3동\n",
      "8770   31  2300.0  0.4     12   중곡제3동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "중곡제3동_습도_202201_202212.csv\n",
      "      day    hour    습도  month address\n",
      "0       1     0.0  35.0      1   중곡제3동\n",
      "1       1   100.0  30.0      1   중곡제3동\n",
      "2       1   200.0  26.0      1   중곡제3동\n",
      "3       1   300.0  26.0      1   중곡제3동\n",
      "4       1   400.0  29.0      1   중곡제3동\n",
      "...   ...     ...   ...    ...     ...\n",
      "8766   31  1900.0  46.0     12   중곡제3동\n",
      "8767   31  2000.0  49.0     12   중곡제3동\n",
      "8768   31  2100.0  58.0     12   중곡제3동\n",
      "8769   31  2200.0  56.0     12   중곡제3동\n",
      "8770   31  2300.0  57.0     12   중곡제3동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "중곡제3동_풍속_202201_202212.csv\n",
      "      day    hour   풍속  month address\n",
      "0       1     0.0  1.0      1   중곡제3동\n",
      "1       1   100.0  0.8      1   중곡제3동\n",
      "2       1   200.0  0.9      1   중곡제3동\n",
      "3       1   300.0  0.8      1   중곡제3동\n",
      "4       1   400.0  1.0      1   중곡제3동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.9     12   중곡제3동\n",
      "8767   31  2000.0  1.4     12   중곡제3동\n",
      "8768   31  2100.0  0.3     12   중곡제3동\n",
      "8769   31  2200.0  0.7     12   중곡제3동\n",
      "8770   31  2300.0  1.1     12   중곡제3동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "중곡제3동_풍향_202201_202212.csv\n",
      "      day    hour     풍향  month address\n",
      "0       1     0.0   90.0      1   중곡제3동\n",
      "1       1   100.0   19.0      1   중곡제3동\n",
      "2       1   200.0  143.0      1   중곡제3동\n",
      "3       1   300.0  309.0      1   중곡제3동\n",
      "4       1   400.0   54.0      1   중곡제3동\n",
      "...   ...     ...    ...    ...     ...\n",
      "8766   31  1900.0  303.0     12   중곡제3동\n",
      "8767   31  2000.0  314.0     12   중곡제3동\n",
      "8768   31  2100.0   97.0     12   중곡제3동\n",
      "8769   31  2200.0  275.0     12   중곡제3동\n",
      "8770   31  2300.0  128.0     12   중곡제3동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "first_dong\n",
      "중곡제4동_강수_202201_202212.csv\n",
      "      day    hour   강수  month address\n",
      "0       1     0.0  0.0      1   중곡제4동\n",
      "1       1   100.0  0.0      1   중곡제4동\n",
      "2       1   200.0  0.0      1   중곡제4동\n",
      "3       1   300.0  0.0      1   중곡제4동\n",
      "4       1   400.0  0.0      1   중곡제4동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.0     12   중곡제4동\n",
      "8767   31  2000.0  0.0     12   중곡제4동\n",
      "8768   31  2100.0  0.0     12   중곡제4동\n",
      "8769   31  2200.0  0.0     12   중곡제4동\n",
      "8770   31  2300.0  0.0     12   중곡제4동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "중곡제4동_기온_202201_202212.csv\n",
      "      day    hour   기온  month address\n",
      "0       1     0.0 -6.1      1   중곡제4동\n",
      "1       1   100.0 -4.3      1   중곡제4동\n",
      "2       1   200.0 -2.5      1   중곡제4동\n",
      "3       1   300.0 -0.9      1   중곡제4동\n",
      "4       1   400.0 -0.3      1   중곡제4동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.8     12   중곡제4동\n",
      "8767   31  2000.0  0.9     12   중곡제4동\n",
      "8768   31  2100.0 -0.4     12   중곡제4동\n",
      "8769   31  2200.0  0.0     12   중곡제4동\n",
      "8770   31  2300.0  0.4     12   중곡제4동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "중곡제4동_습도_202201_202212.csv\n",
      "      day    hour    습도  month address\n",
      "0       1     0.0  35.0      1   중곡제4동\n",
      "1       1   100.0  30.0      1   중곡제4동\n",
      "2       1   200.0  26.0      1   중곡제4동\n",
      "3       1   300.0  26.0      1   중곡제4동\n",
      "4       1   400.0  29.0      1   중곡제4동\n",
      "...   ...     ...   ...    ...     ...\n",
      "8766   31  1900.0  46.0     12   중곡제4동\n",
      "8767   31  2000.0  49.0     12   중곡제4동\n",
      "8768   31  2100.0  58.0     12   중곡제4동\n",
      "8769   31  2200.0  56.0     12   중곡제4동\n",
      "8770   31  2300.0  57.0     12   중곡제4동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "중곡제4동_풍속_202201_202212.csv\n",
      "      day    hour   풍속  month address\n",
      "0       1     0.0  1.0      1   중곡제4동\n",
      "1       1   100.0  0.8      1   중곡제4동\n",
      "2       1   200.0  0.9      1   중곡제4동\n",
      "3       1   300.0  0.8      1   중곡제4동\n",
      "4       1   400.0  1.0      1   중곡제4동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.9     12   중곡제4동\n",
      "8767   31  2000.0  1.4     12   중곡제4동\n",
      "8768   31  2100.0  0.3     12   중곡제4동\n",
      "8769   31  2200.0  0.7     12   중곡제4동\n",
      "8770   31  2300.0  1.1     12   중곡제4동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "중곡제4동_풍향_202201_202212.csv\n",
      "      day    hour     풍향  month address\n",
      "0       1     0.0   90.0      1   중곡제4동\n",
      "1       1   100.0   19.0      1   중곡제4동\n",
      "2       1   200.0  143.0      1   중곡제4동\n",
      "3       1   300.0  309.0      1   중곡제4동\n",
      "4       1   400.0   54.0      1   중곡제4동\n",
      "...   ...     ...    ...    ...     ...\n",
      "8766   31  1900.0  303.0     12   중곡제4동\n",
      "8767   31  2000.0  314.0     12   중곡제4동\n",
      "8768   31  2100.0   97.0     12   중곡제4동\n",
      "8769   31  2200.0  275.0     12   중곡제4동\n",
      "8770   31  2300.0  128.0     12   중곡제4동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "first_dong\n",
      "자양제1동_강수_202201_202212.csv\n",
      "      day    hour   강수  month address\n",
      "0       1     0.0  0.0      1   자양제1동\n",
      "1       1   100.0  0.0      1   자양제1동\n",
      "2       1   200.0  0.0      1   자양제1동\n",
      "3       1   300.0  0.0      1   자양제1동\n",
      "4       1   400.0  0.0      1   자양제1동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.0     12   자양제1동\n",
      "8767   31  2000.0  0.0     12   자양제1동\n",
      "8768   31  2100.0  0.0     12   자양제1동\n",
      "8769   31  2200.0  0.0     12   자양제1동\n",
      "8770   31  2300.0  0.0     12   자양제1동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "자양제1동_기온_202201_202212.csv\n",
      "      day    hour   기온  month address\n",
      "0       1     0.0 -6.5      1   자양제1동\n",
      "1       1   100.0 -5.3      1   자양제1동\n",
      "2       1   200.0 -3.0      1   자양제1동\n",
      "3       1   300.0 -1.9      1   자양제1동\n",
      "4       1   400.0 -0.2      1   자양제1동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.3     12   자양제1동\n",
      "8767   31  2000.0  0.3     12   자양제1동\n",
      "8768   31  2100.0 -1.2     12   자양제1동\n",
      "8769   31  2200.0 -1.0     12   자양제1동\n",
      "8770   31  2300.0  0.0     12   자양제1동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "자양제1동_습도_202201_202212.csv\n",
      "      day    hour    습도  month address\n",
      "0       1     0.0  40.0      1   자양제1동\n",
      "1       1   100.0  30.0      1   자양제1동\n",
      "2       1   200.0  27.0      1   자양제1동\n",
      "3       1   300.0  25.0      1   자양제1동\n",
      "4       1   400.0  29.0      1   자양제1동\n",
      "...   ...     ...   ...    ...     ...\n",
      "8766   31  1900.0  48.0     12   자양제1동\n",
      "8767   31  2000.0  50.0     12   자양제1동\n",
      "8768   31  2100.0  63.0     12   자양제1동\n",
      "8769   31  2200.0  64.0     12   자양제1동\n",
      "8770   31  2300.0  58.0     12   자양제1동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "자양제1동_풍속_202201_202212.csv\n",
      "      day    hour   풍속  month address\n",
      "0       1     0.0  1.0      1   자양제1동\n",
      "1       1   100.0  0.3      1   자양제1동\n",
      "2       1   200.0  0.7      1   자양제1동\n",
      "3       1   300.0  0.7      1   자양제1동\n",
      "4       1   400.0  1.0      1   자양제1동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.4     12   자양제1동\n",
      "8767   31  2000.0  0.9     12   자양제1동\n",
      "8768   31  2100.0  0.6     12   자양제1동\n",
      "8769   31  2200.0  0.9     12   자양제1동\n",
      "8770   31  2300.0  1.4     12   자양제1동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "자양제1동_풍향_202201_202212.csv\n",
      "      day    hour     풍향  month address\n",
      "0       1     0.0  103.0      1   자양제1동\n",
      "1       1   100.0   71.0      1   자양제1동\n",
      "2       1   200.0  156.0      1   자양제1동\n",
      "3       1   300.0   59.0      1   자양제1동\n",
      "4       1   400.0   59.0      1   자양제1동\n",
      "...   ...     ...    ...    ...     ...\n",
      "8766   31  1900.0  292.0     12   자양제1동\n",
      "8767   31  2000.0  268.0     12   자양제1동\n",
      "8768   31  2100.0  275.0     12   자양제1동\n",
      "8769   31  2200.0  267.0     12   자양제1동\n",
      "8770   31  2300.0  261.0     12   자양제1동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "first_dong\n",
      "자양제2동_강수_202201_202212.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      day    hour   강수  month address\n",
      "0       1     0.0  0.0      1   자양제2동\n",
      "1       1   100.0  0.0      1   자양제2동\n",
      "2       1   200.0  0.0      1   자양제2동\n",
      "3       1   300.0  0.0      1   자양제2동\n",
      "4       1   400.0  0.0      1   자양제2동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.0     12   자양제2동\n",
      "8767   31  2000.0  0.0     12   자양제2동\n",
      "8768   31  2100.0  0.0     12   자양제2동\n",
      "8769   31  2200.0  0.0     12   자양제2동\n",
      "8770   31  2300.0  0.0     12   자양제2동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "자양제2동_기온_202201_202212.csv\n",
      "      day    hour   기온  month address\n",
      "0       1     0.0 -6.5      1   자양제2동\n",
      "1       1   100.0 -5.3      1   자양제2동\n",
      "2       1   200.0 -3.0      1   자양제2동\n",
      "3       1   300.0 -1.9      1   자양제2동\n",
      "4       1   400.0 -0.2      1   자양제2동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.3     12   자양제2동\n",
      "8767   31  2000.0  0.3     12   자양제2동\n",
      "8768   31  2100.0 -1.2     12   자양제2동\n",
      "8769   31  2200.0 -1.0     12   자양제2동\n",
      "8770   31  2300.0  0.0     12   자양제2동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "자양제2동_습도_202201_202212.csv\n",
      "      day    hour    습도  month address\n",
      "0       1     0.0  40.0      1   자양제2동\n",
      "1       1   100.0  30.0      1   자양제2동\n",
      "2       1   200.0  27.0      1   자양제2동\n",
      "3       1   300.0  25.0      1   자양제2동\n",
      "4       1   400.0  29.0      1   자양제2동\n",
      "...   ...     ...   ...    ...     ...\n",
      "8766   31  1900.0  48.0     12   자양제2동\n",
      "8767   31  2000.0  50.0     12   자양제2동\n",
      "8768   31  2100.0  63.0     12   자양제2동\n",
      "8769   31  2200.0  64.0     12   자양제2동\n",
      "8770   31  2300.0  58.0     12   자양제2동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "자양제2동_풍속_202201_202212.csv\n",
      "      day    hour   풍속  month address\n",
      "0       1     0.0  1.0      1   자양제2동\n",
      "1       1   100.0  0.3      1   자양제2동\n",
      "2       1   200.0  0.7      1   자양제2동\n",
      "3       1   300.0  0.7      1   자양제2동\n",
      "4       1   400.0  1.0      1   자양제2동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.4     12   자양제2동\n",
      "8767   31  2000.0  0.9     12   자양제2동\n",
      "8768   31  2100.0  0.6     12   자양제2동\n",
      "8769   31  2200.0  0.9     12   자양제2동\n",
      "8770   31  2300.0  1.4     12   자양제2동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "자양제2동_풍향_202201_202212.csv\n",
      "      day    hour     풍향  month address\n",
      "0       1     0.0  103.0      1   자양제2동\n",
      "1       1   100.0   71.0      1   자양제2동\n",
      "2       1   200.0  156.0      1   자양제2동\n",
      "3       1   300.0   59.0      1   자양제2동\n",
      "4       1   400.0   59.0      1   자양제2동\n",
      "...   ...     ...    ...    ...     ...\n",
      "8766   31  1900.0  292.0     12   자양제2동\n",
      "8767   31  2000.0  268.0     12   자양제2동\n",
      "8768   31  2100.0  275.0     12   자양제2동\n",
      "8769   31  2200.0  267.0     12   자양제2동\n",
      "8770   31  2300.0  261.0     12   자양제2동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "first_dong\n",
      "자양제3동_강수_202201_202212.csv\n",
      "      day    hour   강수  month address\n",
      "0       1     0.0  0.0      1   자양제3동\n",
      "1       1   100.0  0.0      1   자양제3동\n",
      "2       1   200.0  0.0      1   자양제3동\n",
      "3       1   300.0  0.0      1   자양제3동\n",
      "4       1   400.0  0.0      1   자양제3동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.0     12   자양제3동\n",
      "8767   31  2000.0  0.0     12   자양제3동\n",
      "8768   31  2100.0  0.0     12   자양제3동\n",
      "8769   31  2200.0  0.0     12   자양제3동\n",
      "8770   31  2300.0  0.0     12   자양제3동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "자양제3동_기온_202201_202212.csv\n",
      "      day    hour   기온  month address\n",
      "0       1     0.0 -3.5      1   자양제3동\n",
      "1       1   100.0 -2.1      1   자양제3동\n",
      "2       1   200.0 -1.4      1   자양제3동\n",
      "3       1   300.0  0.5      1   자양제3동\n",
      "4       1   400.0  1.8      1   자양제3동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.5     12   자양제3동\n",
      "8767   31  2000.0  0.2     12   자양제3동\n",
      "8768   31  2100.0 -0.3     12   자양제3동\n",
      "8769   31  2200.0 -0.7     12   자양제3동\n",
      "8770   31  2300.0  0.0     12   자양제3동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "자양제3동_습도_202201_202212.csv\n",
      "      day    hour    습도  month address\n",
      "0       1     0.0  36.0      1   자양제3동\n",
      "1       1   100.0  35.0      1   자양제3동\n",
      "2       1   200.0  30.0      1   자양제3동\n",
      "3       1   300.0  30.0      1   자양제3동\n",
      "4       1   400.0  30.0      1   자양제3동\n",
      "...   ...     ...   ...    ...     ...\n",
      "8766   31  1900.0  46.0     12   자양제3동\n",
      "8767   31  2000.0  54.0     12   자양제3동\n",
      "8768   31  2100.0  62.0     12   자양제3동\n",
      "8769   31  2200.0  66.0     12   자양제3동\n",
      "8770   31  2300.0  59.0     12   자양제3동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "자양제3동_풍속_202201_202212.csv\n",
      "      day   hour          풍속  month address\n",
      "0       1   0000   1.400000       1   자양제3동\n",
      "1       1   0100   1.700000       1   자양제3동\n",
      "2       1   0200   0.600000       1   자양제3동\n",
      "3       1   0300   1.000000       1   자양제3동\n",
      "4       1   0400   1.200000       1   자양제3동\n",
      "...   ...    ...         ...    ...     ...\n",
      "8766   31   1900   3.100000      12   자양제3동\n",
      "8767   31   2000   0.600000      12   자양제3동\n",
      "8768   31   2100   0.400000      12   자양제3동\n",
      "8769   31   2200   0.400000      12   자양제3동\n",
      "8770   31   2300   1.400000      12   자양제3동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "자양제3동_풍향_202201_202212.csv\n",
      "      day    hour     풍향  month address\n",
      "0       1     0.0   39.0      1   자양제3동\n",
      "1       1   100.0  200.0      1   자양제3동\n",
      "2       1   200.0  353.0      1   자양제3동\n",
      "3       1   300.0  187.0      1   자양제3동\n",
      "4       1   400.0  148.0      1   자양제3동\n",
      "...   ...     ...    ...    ...     ...\n",
      "8766   31  1900.0  295.0     12   자양제3동\n",
      "8767   31  2000.0  264.0     12   자양제3동\n",
      "8768   31  2100.0  242.0     12   자양제3동\n",
      "8769   31  2200.0  216.0     12   자양제3동\n",
      "8770   31  2300.0  224.0     12   자양제3동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "first_dong\n",
      "자양제4동_강수_202201_202212.csv\n",
      "      day    hour   강수  month address\n",
      "0       1     0.0  0.0      1   자양제4동\n",
      "1       1   100.0  0.0      1   자양제4동\n",
      "2       1   200.0  0.0      1   자양제4동\n",
      "3       1   300.0  0.0      1   자양제4동\n",
      "4       1   400.0  0.0      1   자양제4동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.0     12   자양제4동\n",
      "8767   31  2000.0  0.0     12   자양제4동\n",
      "8768   31  2100.0  0.0     12   자양제4동\n",
      "8769   31  2200.0  0.0     12   자양제4동\n",
      "8770   31  2300.0  0.0     12   자양제4동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "자양제4동_기온_202201_202212.csv\n",
      "      day    hour   기온  month address\n",
      "0       1     0.0 -3.5      1   자양제4동\n",
      "1       1   100.0 -2.1      1   자양제4동\n",
      "2       1   200.0 -1.4      1   자양제4동\n",
      "3       1   300.0  0.5      1   자양제4동\n",
      "4       1   400.0  1.8      1   자양제4동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.5     12   자양제4동\n",
      "8767   31  2000.0  0.2     12   자양제4동\n",
      "8768   31  2100.0 -0.3     12   자양제4동\n",
      "8769   31  2200.0 -0.7     12   자양제4동\n",
      "8770   31  2300.0  0.0     12   자양제4동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "자양제4동_습도_202201_202212.csv\n",
      "      day    hour    습도  month address\n",
      "0       1     0.0  36.0      1   자양제4동\n",
      "1       1   100.0  35.0      1   자양제4동\n",
      "2       1   200.0  30.0      1   자양제4동\n",
      "3       1   300.0  30.0      1   자양제4동\n",
      "4       1   400.0  30.0      1   자양제4동\n",
      "...   ...     ...   ...    ...     ...\n",
      "8766   31  1900.0  46.0     12   자양제4동\n",
      "8767   31  2000.0  54.0     12   자양제4동\n",
      "8768   31  2100.0  62.0     12   자양제4동\n",
      "8769   31  2200.0  66.0     12   자양제4동\n",
      "8770   31  2300.0  59.0     12   자양제4동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "자양제4동_풍속_202201_202212.csv\n",
      "      day   hour          풍속  month address\n",
      "0       1   0000   1.400000       1   자양제4동\n",
      "1       1   0100   1.700000       1   자양제4동\n",
      "2       1   0200   0.600000       1   자양제4동\n",
      "3       1   0300   1.000000       1   자양제4동\n",
      "4       1   0400   1.200000       1   자양제4동\n",
      "...   ...    ...         ...    ...     ...\n",
      "8766   31   1900   3.100000      12   자양제4동\n",
      "8767   31   2000   0.600000      12   자양제4동\n",
      "8768   31   2100   0.400000      12   자양제4동\n",
      "8769   31   2200   0.400000      12   자양제4동\n",
      "8770   31   2300   1.400000      12   자양제4동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "자양제4동_풍향_202201_202212.csv\n",
      "      day    hour     풍향  month address\n",
      "0       1     0.0   39.0      1   자양제4동\n",
      "1       1   100.0  200.0      1   자양제4동\n",
      "2       1   200.0  353.0      1   자양제4동\n",
      "3       1   300.0  187.0      1   자양제4동\n",
      "4       1   400.0  148.0      1   자양제4동\n",
      "...   ...     ...    ...    ...     ...\n",
      "8766   31  1900.0  295.0     12   자양제4동\n",
      "8767   31  2000.0  264.0     12   자양제4동\n",
      "8768   31  2100.0  242.0     12   자양제4동\n",
      "8769   31  2200.0  216.0     12   자양제4동\n",
      "8770   31  2300.0  224.0     12   자양제4동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "first_dong\n",
      "화양동_강수_202201_202212.csv\n",
      "      day    hour   강수  month address\n",
      "0       1     0.0  0.0      1     화양동\n",
      "1       1   100.0  0.0      1     화양동\n",
      "2       1   200.0  0.0      1     화양동\n",
      "3       1   300.0  0.0      1     화양동\n",
      "4       1   400.0  0.0      1     화양동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  0.0     12     화양동\n",
      "8767   31  2000.0  0.0     12     화양동\n",
      "8768   31  2100.0  0.0     12     화양동\n",
      "8769   31  2200.0  0.0     12     화양동\n",
      "8770   31  2300.0  0.0     12     화양동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "화양동_기온_202201_202212.csv\n",
      "      day    hour   기온  month address\n",
      "0       1     0.0 -3.5      1     화양동\n",
      "1       1   100.0 -2.1      1     화양동\n",
      "2       1   200.0 -1.4      1     화양동\n",
      "3       1   300.0  0.5      1     화양동\n",
      "4       1   400.0  1.8      1     화양동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  1.5     12     화양동\n",
      "8767   31  2000.0  0.2     12     화양동\n",
      "8768   31  2100.0 -0.3     12     화양동\n",
      "8769   31  2200.0 -0.7     12     화양동\n",
      "8770   31  2300.0  0.0     12     화양동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "화양동_습도_202201_202212.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      day    hour    습도  month address\n",
      "0       1     0.0  36.0      1     화양동\n",
      "1       1   100.0  35.0      1     화양동\n",
      "2       1   200.0  30.0      1     화양동\n",
      "3       1   300.0  30.0      1     화양동\n",
      "4       1   400.0  30.0      1     화양동\n",
      "...   ...     ...   ...    ...     ...\n",
      "8766   31  1900.0  46.0     12     화양동\n",
      "8767   31  2000.0  54.0     12     화양동\n",
      "8768   31  2100.0  62.0     12     화양동\n",
      "8769   31  2200.0  66.0     12     화양동\n",
      "8770   31  2300.0  59.0     12     화양동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "화양동_풍속_202201_202212.csv\n",
      "      day    hour   풍속  month address\n",
      "0       1     0.0  1.4      1     화양동\n",
      "1       1   100.0  1.7      1     화양동\n",
      "2       1   200.0  0.6      1     화양동\n",
      "3       1   300.0  1.0      1     화양동\n",
      "4       1   400.0  1.2      1     화양동\n",
      "...   ...     ...  ...    ...     ...\n",
      "8766   31  1900.0  3.1     12     화양동\n",
      "8767   31  2000.0  0.6     12     화양동\n",
      "8768   31  2100.0  0.4     12     화양동\n",
      "8769   31  2200.0  0.4     12     화양동\n",
      "8770   31  2300.0  1.4     12     화양동\n",
      "\n",
      "[8771 rows x 5 columns]\n",
      "화양동_풍향_202201_202212.csv\n",
      "      day    hour     풍향  month address\n",
      "0       1     0.0   39.0      1     화양동\n",
      "1       1   100.0  200.0      1     화양동\n",
      "2       1   200.0  353.0      1     화양동\n",
      "3       1   300.0  187.0      1     화양동\n",
      "4       1   400.0  148.0      1     화양동\n",
      "...   ...     ...    ...    ...     ...\n",
      "8766   31  1900.0  295.0     12     화양동\n",
      "8767   31  2000.0  264.0     12     화양동\n",
      "8768   31  2100.0  242.0     12     화양동\n",
      "8769   31  2200.0  216.0     12     화양동\n",
      "8770   31  2300.0  224.0     12     화양동\n",
      "\n",
      "[8771 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "#전체 파일 목록 불러오기\n",
    "path = \"./weather_datasets/\"\n",
    "file_list = os.listdir(path)\n",
    "\n",
    "#동별로 파일 리스트 만들기\n",
    "dongs = ['광장동', '구의제1동', '구의제2동', '구의제3동', '군자동', '능동', '중곡제1동', '중곡제2동', '중곡제3동', '중곡제4동'\n",
    "         ,'자양제1동', '자양제2동', '자양제3동', '자양제4동',  '화양동']\n",
    "\n",
    "# 1.광장동~능동  2. 중곡제1동~자양제2동  4. 자양제3동~화양동\n",
    "first_dong = True\n",
    "for dong in dongs:\n",
    "    file_list_csv = [file for file in file_list if file.startswith(dong)]\n",
    "    print('first_dong')\n",
    "    #동별로 데이터프레임 만들기\n",
    "    \n",
    "    first_data = True\n",
    "    for file in file_list_csv:\n",
    "        print(file)\n",
    "        #현재 file을 temp_df에 저장하기\n",
    "        temp_df = pd.read_csv(path+file)\n",
    "        \n",
    "        #데이터 이상치 제거. 구의제1동, 자양제3, 4동의 풍속 데이터에서 오류 있음\n",
    "        if len(temp_df['hour']) != 8771:\n",
    "            temp_df = temp_df.iloc[:8771,]\n",
    "\n",
    "        #month 열 추가\n",
    "        months = temp_df.loc[temp_df[' format: day'].str.contains('Start')].index\n",
    "        months = months.append(pd.Index([len(temp_df['hour'])]))\n",
    "\n",
    "        month_list = []\n",
    "        start = 0\n",
    "        month = 1\n",
    "        for month_idx in months:\n",
    "            temp_month_list = [month for i in range(month_idx-start)]\n",
    "            month_list.extend(temp_month_list)\n",
    "            start = month_idx\n",
    "            month += 1\n",
    "            \n",
    "        temp_df['month'] = month_list\n",
    "\n",
    "        #행정동 추가\n",
    "        temp_df['address'] = file.split('_')[0]\n",
    "\n",
    "        #열이름 바꾸기\n",
    "        temp_df.columns = ['day', 'hour', file.split('_')[1], 'month', 'address']\n",
    "        print(temp_df)\n",
    "        #첫 데이터프레임일 경우(강수) 기본 데이터프레임으로 설정, 아닐 경우(기온, 습도, 풍속, 풍향) 기본 데이터프레임에 merge\n",
    "        if first_data == True:\n",
    "            df = temp_df\n",
    "        else:\n",
    "            temp_df = temp_df.drop(['day', 'hour', 'month', 'address'], axis=1)\n",
    "            df = pd.concat([df, temp_df], axis=1)\n",
    "\n",
    "        first_data = False\n",
    "\n",
    "\n",
    "    df = df[['address', 'month', 'day', 'hour', '강수', '기온', '습도', '풍속', '풍향']]\n",
    "    \n",
    "    if first_dong == True:\n",
    "        total_df = df\n",
    "    else:\n",
    "        total_df = pd.concat([total_df, df])\n",
    "        \n",
    "    first_dong = False\n",
    "    \n",
    "#null인 행 모두 제거    \n",
    "total_df = total_df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "afb8d888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>강수</th>\n",
       "      <th>기온</th>\n",
       "      <th>습도</th>\n",
       "      <th>풍속</th>\n",
       "      <th>풍향</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>광장동</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.5</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>광장동</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>광장동</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>광장동</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>광장동</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8766</th>\n",
       "      <td>화양동</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8767</th>\n",
       "      <td>화양동</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8768</th>\n",
       "      <td>화양동</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8769</th>\n",
       "      <td>화양동</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8770</th>\n",
       "      <td>화양동</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>224.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>131400 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     address  month  day    hour   강수   기온    습도   풍속     풍향\n",
       "0        광장동      1    1     0.0  0.0 -6.5  40.0  1.0  103.0\n",
       "1        광장동      1    1   100.0  0.0 -5.3  30.0  0.3   71.0\n",
       "2        광장동      1    1   200.0  0.0 -3.0  27.0  0.7  156.0\n",
       "3        광장동      1    1   300.0  0.0 -1.9  25.0  0.7   59.0\n",
       "4        광장동      1    1   400.0  0.0 -0.2  29.0  1.0   59.0\n",
       "...      ...    ...  ...     ...  ...  ...   ...  ...    ...\n",
       "8766     화양동     12   31  1900.0  0.0  1.5  46.0  3.1  295.0\n",
       "8767     화양동     12   31  2000.0  0.0  0.2  54.0  0.6  264.0\n",
       "8768     화양동     12   31  2100.0  0.0 -0.3  62.0  0.4  242.0\n",
       "8769     화양동     12   31  2200.0  0.0 -0.7  66.0  0.4  216.0\n",
       "8770     화양동     12   31  2300.0  0.0  0.0  59.0  1.4  224.0\n",
       "\n",
       "[131400 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7823d357",
   "metadata": {},
   "source": [
    "## 데이터 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7f8f04fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 131400 entries, 0 to 8770\n",
      "Data columns (total 9 columns):\n",
      " #   Column   Non-Null Count   Dtype  \n",
      "---  ------   --------------   -----  \n",
      " 0   address  131400 non-null  object \n",
      " 1   month    131400 non-null  int64  \n",
      " 2   day      131400 non-null  object \n",
      " 3   hour     131400 non-null  float64\n",
      " 4   강수       131400 non-null  float64\n",
      " 5   기온       131400 non-null  float64\n",
      " 6   습도       131400 non-null  float64\n",
      " 7   풍속       131400 non-null  object \n",
      " 8   풍향       131400 non-null  float64\n",
      "dtypes: float64(5), int64(1), object(3)\n",
      "memory usage: 10.0+ MB\n"
     ]
    }
   ],
   "source": [
    "total_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "43581575",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>강수</th>\n",
       "      <th>기온</th>\n",
       "      <th>습도</th>\n",
       "      <th>풍속</th>\n",
       "      <th>풍향</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>131400.000000</td>\n",
       "      <td>131400.000000</td>\n",
       "      <td>131400.000000</td>\n",
       "      <td>131400.000000</td>\n",
       "      <td>131400.000000</td>\n",
       "      <td>131400.000000</td>\n",
       "      <td>131400.000000</td>\n",
       "      <td>131400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.526027</td>\n",
       "      <td>15.720548</td>\n",
       "      <td>1150.000000</td>\n",
       "      <td>0.208524</td>\n",
       "      <td>13.594966</td>\n",
       "      <td>56.617656</td>\n",
       "      <td>1.661507</td>\n",
       "      <td>178.195038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.447864</td>\n",
       "      <td>8.796280</td>\n",
       "      <td>692.221289</td>\n",
       "      <td>1.617046</td>\n",
       "      <td>11.453992</td>\n",
       "      <td>19.871630</td>\n",
       "      <td>1.029864</td>\n",
       "      <td>100.153289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-50.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>575.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>189.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.200001</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>270.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2300.000000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>35.799999</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>360.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               month            day           hour             강수  \\\n",
       "count  131400.000000  131400.000000  131400.000000  131400.000000   \n",
       "mean        6.526027      15.720548    1150.000000       0.208524   \n",
       "std         3.447864       8.796280     692.221289       1.617046   \n",
       "min         1.000000       1.000000       0.000000      -1.000000   \n",
       "25%         4.000000       8.000000     575.000000       0.000000   \n",
       "50%         7.000000      16.000000    1150.000000       0.000000   \n",
       "75%        10.000000      23.000000    1725.000000       0.000000   \n",
       "max        12.000000      31.000000    2300.000000      46.500000   \n",
       "\n",
       "                  기온             습도             풍속             풍향  \n",
       "count  131400.000000  131400.000000  131400.000000  131400.000000  \n",
       "mean       13.594966      56.617656       1.661507     178.195038  \n",
       "std        11.453992      19.871630       1.029864     100.153289  \n",
       "min       -50.000000      -1.000000      -1.000000      -1.000000  \n",
       "25%         4.900000      41.000000       0.900000      83.000000  \n",
       "50%        15.100000      57.000000       1.500000     189.000000  \n",
       "75%        23.200001      72.000000       2.300000     270.000000  \n",
       "max        35.799999     100.000000       8.300000     360.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_df['풍속'] = total_df['풍속'].astype('float')\n",
    "total_df['day'] = total_df['day'].astype('int')\n",
    "total_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0bfc7b42",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gj = total_df[total_df['address'] == '광장동']\n",
    "df_jg1 = total_df[total_df['address'] == '중곡제1동']\n",
    "df_hy = total_df[total_df['address'] == '화양동']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d84c92e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "풍속\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gj_month_wv = df_gj[['month', '풍속']].groupby(['month']).mean()\n",
    "jg1_month_wv = df_jg1[['month', '풍속']].groupby(['month']).mean()\n",
    "hy_month_wv = df_hy[['month', '풍속']].groupby(['month']).mean()\n",
    "\n",
    "\n",
    "plt.plot(gj_month_wv)\n",
    "plt.plot(jg1_month_wv)\n",
    "plt.plot(hy_month_wv)\n",
    "\n",
    "print('풍속')\n",
    "plt.show()\n",
    "\n",
    "# 파란색-광장동, 주황색-중곡동, 초록색-화양동"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dac95d00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "습도\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gj_month_wv = df_gj[['month', '습도']].groupby(['month']).mean()\n",
    "jg1_month_wv = df_jg1[['month', '습도']].groupby(['month']).mean()\n",
    "hy_month_wv = df_hy[['month', '습도']].groupby(['month']).mean()\n",
    "\n",
    "\n",
    "plt.plot(gj_month_wv)\n",
    "plt.plot(jg1_month_wv)\n",
    "plt.plot(hy_month_wv)\n",
    "\n",
    "print('습도')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d1c9e439",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "기온\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gj_month_wv = df_gj[['month', '기온']].groupby(['month']).mean()\n",
    "jg1_month_wv = df_jg1[['month', '기온']].groupby(['month']).mean()\n",
    "hy_month_wv = df_hy[['month', '기온']].groupby(['month']).mean()\n",
    "\n",
    "\n",
    "plt.plot(gj_month_wv)\n",
    "plt.plot(jg1_month_wv)\n",
    "plt.plot(hy_month_wv)\n",
    "\n",
    "print('기온')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34407c68",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29b52a2f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23bf6d1c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f5d26aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdfbcd99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e856bf4f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddfde92c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
